{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder for Anomaly Detection with scikit-learn, Keras and TensorFlow\n",
    "\n",
    "This script trains an autoencoder for anomaly detection. We use Python, scikit-learn, TensorFlow and Keras to prepare the data and train the model.\n",
    "\n",
    "The input data is sensor data. Here is one example:\n",
    "\n",
    "\"Time\",\"V1\",\"V2\",\"V3\",\"V4\",\"V5\",\"V6\",\"V7\",\"V8\",\"V9\",\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V17\",\"V18\",\"V19\",\"V20\",\"V21\",\"V22\",\"V23\",\"V24\",\"V25\",\"V26\",\"V27\",\"V28\",\"Amount\",\"Class\"\n",
    "\n",
    "0,-1.3598071336738,-0.0727811733098497,2.53634673796914,1.37815522427443,-0.338320769942518,0.462387777762292,0.239598554061257,0.0986979012610507,0.363786969611213,0.0907941719789316,-0.551599533260813,-0.617800855762348,-0.991389847235408,-0.311169353699879,1.46817697209427,-0.470400525259478,0.207971241929242,0.0257905801985591,0.403992960255733,0.251412098239705,-0.018306777944153,0.277837575558899,-0.110473910188767,0.0669280749146731,0.128539358273528,-0.189114843888824,0.133558376740387,-0.0210530534538215,149.62,\"0\"\n",
    "\n",
    "The autoencoder compresses the data and tries to reconstruct it. The reconstruction error finds anomalies.\n",
    "\n",
    "This Jupyter notebook was tested with Python 3.6.7, Numpy 1.16.4, scikit-learn 0.20.1 and tensorflow 2.0.0-alpha0. \n",
    "\n",
    "Kudos to David Ellison who created the foundation for this example: https://www.datascience.com/blog/fraud-detection-with-tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "# matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_curve\n",
    "from sklearn.metrics import recall_score, classification_report, auc, roc_curve\n",
    "from sklearn.metrics import precision_recall_fscore_support, f1_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from pylab import rcParams\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input, Dense\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "#check TensorFlow verson: 1.x or 2.0?\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set random seed and percentage of test data\n",
    "RANDOM_SEED = 314 #used to help randomly select the data points\n",
    "TEST_PCT = 0.2 # 20% of the data\n",
    "\n",
    "#set up graphic style in this case I am using the color scheme from xkcd.com\n",
    "rcParams['figure.figsize'] = 14, 8.7 # Golden Mean\n",
    "LABELS = [\"Normal\",\"Fraud\"]\n",
    "#col_list = [\"cerulean\",\"scarlet\"]# https://xkcd.com/color/rgb/\n",
    "#sns.set(style='white', font_scale=1.75, palette=sns.xkcd_palette(col_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../data/sensor_data.csv\") #unzip and read in data downloaded to the local directory\n",
    "df.head(n=5) #just to check you imported the dataset properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #secondary check on the size of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any() #check to see if any values are null, which there are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['Class'], sort = True) #class comparison 0=Normal 1=Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#if you don't have an intuitive sense of how imbalanced these two classes are, let's go visual\n",
    "count_classes = pd.value_counts(df['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.title(\"Frequency by observation number\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Number of Observations\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_df = df[df.Class == 0] #save normal_df observations into a separate df\n",
    "fraud_df = df[df.Class == 1] #do the same for frauds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_df.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_df.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot of high value transactions\n",
    "bins = np.linspace(200, 2500, 100)\n",
    "plt.hist(normal_df.Amount, bins, alpha=1, normed=True, label='Normal')\n",
    "plt.hist(fraud_df.Amount, bins, alpha=0.6, normed=True, label='Fraud')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Amount by percentage of transactions (transactions \\$200+)\")\n",
    "plt.xlabel(\"Transaction amount (USD)\")\n",
    "plt.ylabel(\"Percentage of transactions (%)\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/Users/kai.waehner/anaconda/envs/ksql-python/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 48, 48) #48 hours\n",
    "plt.hist((normal_df.Time/(60*60)), bins, alpha=1, normed=True, label='Normal')\n",
    "plt.hist((fraud_df.Time/(60*60)), bins, alpha=0.6, normed=True, label='Fraud')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Percentage of transactions by hour\")\n",
    "plt.xlabel(\"Transaction time as measured from first transaction in the dataset (hours)\")\n",
    "plt.ylabel(\"Percentage of transactions (%)\");\n",
    "#plt.hist((df.Time/(60*60)),bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter((normal_df.Time/(60*60)), normal_df.Amount, alpha=0.6, label='Normal')\n",
    "plt.scatter((fraud_df.Time/(60*60)), fraud_df.Amount, alpha=0.9, label='Fraud')\n",
    "plt.title(\"Amount of transaction by hour\")\n",
    "plt.xlabel(\"Transaction time as measured from first transaction in the dataset (hours)\")\n",
    "plt.ylabel('Amount (USD)')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = df.drop(['Time'], axis=1) #if you think the var is unimportant\n",
    "df_norm = df\n",
    "df_norm['Time'] = StandardScaler().fit_transform(df_norm['Time'].values.reshape(-1, 1))\n",
    "df_norm['Amount'] = StandardScaler().fit_transform(df_norm['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x = train_test_split(df_norm, test_size=TEST_PCT, random_state=RANDOM_SEED)\n",
    "train_x = train_x[train_x.Class == 0] #where normal transactions\n",
    "train_x = train_x.drop(['Class'], axis=1) #drop the class column (as Autoencoder is unsupervised and does not need / use labels for training)\n",
    "\n",
    "# test_x (without class) for validation; test_y (with Class) for prediction + calculating MSE / reconstruction error\n",
    "test_y = test_x['Class'] #save the class column for the test set\n",
    "test_x = test_x.drop(['Class'], axis=1) #drop the class column\n",
    "\n",
    "train_x = train_x.values #transform to ndarray\n",
    "test_x = test_x.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227468, 30)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce number of epochs and batch_size if your Jupyter crashes (due to memory issues)\n",
    "# or if you just want to demo and not run it for so long.\n",
    "# nb_epoch = 100 instead of 5\n",
    "# batch_size = 128 instead of 32\n",
    "nb_epoch = 5\n",
    "batch_size = 32\n",
    "\n",
    "\n",
    "# Autoencoder: 30 => 14 => 7 => 7 => 14 => 30 dimensions\n",
    "input_dim = train_x.shape[1] #num of columns, 30\n",
    "encoding_dim = 14\n",
    "hidden_dim = int(encoding_dim / 2) #i.e. 7\n",
    "learning_rate = 1e-7\n",
    "\n",
    "# Dense = fully connected layer\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "# First parameter is output units (14 then 7 then 7 then 30) :\n",
    "encoder = Dense(encoding_dim, activation=\"tanh\", activity_regularizer=regularizers.l1(learning_rate))(input_layer)\n",
    "encoder = Dense(hidden_dim, activation=\"relu\")(encoder)\n",
    "decoder = Dense(hidden_dim, activation='tanh')(encoder)\n",
    "decoder = Dense(input_dim, activation='relu')(decoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 227468 samples, validate on 56962 samples\n",
      "Epoch 1/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.8958 - accuracy: 0.3266 - val_loss: 0.8625 - val_accuracy: 0.4087\n",
      "Epoch 2/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7823 - accuracy: 0.5437 - val_loss: 0.7870 - val_accuracy: 0.6211\n",
      "Epoch 3/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7440 - accuracy: 0.6288 - val_loss: 0.7706 - val_accuracy: 0.6350\n",
      "Epoch 4/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7320 - accuracy: 0.6426 - val_loss: 0.7606 - val_accuracy: 0.6541\n",
      "Epoch 5/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7238 - accuracy: 0.6603 - val_loss: 0.7550 - val_accuracy: 0.6689\n",
      "Epoch 6/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7187 - accuracy: 0.6684 - val_loss: 0.7511 - val_accuracy: 0.6765\n",
      "Epoch 7/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.7147 - accuracy: 0.6744 - val_loss: 0.7473 - val_accuracy: 0.6789\n",
      "Epoch 8/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7115 - accuracy: 0.6796 - val_loss: 0.7444 - val_accuracy: 0.6835\n",
      "Epoch 9/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7084 - accuracy: 0.6841 - val_loss: 0.7414 - val_accuracy: 0.6841\n",
      "Epoch 10/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7062 - accuracy: 0.6838 - val_loss: 0.7399 - val_accuracy: 0.6873\n",
      "Epoch 11/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.7045 - accuracy: 0.6824 - val_loss: 0.7377 - val_accuracy: 0.6864\n",
      "Epoch 12/100\n",
      "227468/227468 [==============================] - 3s 14us/sample - loss: 0.7031 - accuracy: 0.6801 - val_loss: 0.7368 - val_accuracy: 0.6764\n",
      "Epoch 13/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.7015 - accuracy: 0.6785 - val_loss: 0.7353 - val_accuracy: 0.6820\n",
      "Epoch 14/100\n",
      "227468/227468 [==============================] - 4s 15us/sample - loss: 0.7006 - accuracy: 0.6775 - val_loss: 0.7346 - val_accuracy: 0.6774\n",
      "Epoch 15/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.7002 - accuracy: 0.6775 - val_loss: 0.7340 - val_accuracy: 0.6820\n",
      "Epoch 16/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6991 - accuracy: 0.6771 - val_loss: 0.7335 - val_accuracy: 0.6837\n",
      "Epoch 17/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6991 - accuracy: 0.6783 - val_loss: 0.7329 - val_accuracy: 0.6795\n",
      "Epoch 18/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6977 - accuracy: 0.6803 - val_loss: 0.7318 - val_accuracy: 0.6839\n",
      "Epoch 19/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6977 - accuracy: 0.6808 - val_loss: 0.7317 - val_accuracy: 0.6841\n",
      "Epoch 20/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6970 - accuracy: 0.6818 - val_loss: 0.7314 - val_accuracy: 0.6821\n",
      "Epoch 21/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6966 - accuracy: 0.6830 - val_loss: 0.7319 - val_accuracy: 0.6876\n",
      "Epoch 22/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6965 - accuracy: 0.6824 - val_loss: 0.7309 - val_accuracy: 0.6870\n",
      "Epoch 23/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6964 - accuracy: 0.6838 - val_loss: 0.7313 - val_accuracy: 0.6874\n",
      "Epoch 24/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6957 - accuracy: 0.6843 - val_loss: 0.7313 - val_accuracy: 0.6857\n",
      "Epoch 25/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6955 - accuracy: 0.6843 - val_loss: 0.7309 - val_accuracy: 0.6856\n",
      "Epoch 26/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6955 - accuracy: 0.6850 - val_loss: 0.7306 - val_accuracy: 0.6862\n",
      "Epoch 27/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6949 - accuracy: 0.6857 - val_loss: 0.7312 - val_accuracy: 0.6800\n",
      "Epoch 28/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6946 - accuracy: 0.6856 - val_loss: 0.7296 - val_accuracy: 0.6898\n",
      "Epoch 29/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6946 - accuracy: 0.6864 - val_loss: 0.7298 - val_accuracy: 0.6865\n",
      "Epoch 30/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6941 - accuracy: 0.6867 - val_loss: 0.7294 - val_accuracy: 0.6928\n",
      "Epoch 31/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6937 - accuracy: 0.6879 - val_loss: 0.7287 - val_accuracy: 0.6904\n",
      "Epoch 32/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6937 - accuracy: 0.6882 - val_loss: 0.7292 - val_accuracy: 0.6915\n",
      "Epoch 33/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6936 - accuracy: 0.6887 - val_loss: 0.7283 - val_accuracy: 0.6898\n",
      "Epoch 34/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6928 - accuracy: 0.6901 - val_loss: 0.7315 - val_accuracy: 0.6899\n",
      "Epoch 35/100\n",
      "227468/227468 [==============================] - 4s 18us/sample - loss: 0.6929 - accuracy: 0.6912 - val_loss: 0.7276 - val_accuracy: 0.6939\n",
      "Epoch 36/100\n",
      "227468/227468 [==============================] - 6s 26us/sample - loss: 0.6924 - accuracy: 0.6921 - val_loss: 0.7275 - val_accuracy: 0.6942\n",
      "Epoch 37/100\n",
      "227468/227468 [==============================] - 5s 20us/sample - loss: 0.6924 - accuracy: 0.6924 - val_loss: 0.7273 - val_accuracy: 0.6938\n",
      "Epoch 38/100\n",
      "227468/227468 [==============================] - 4s 19us/sample - loss: 0.6919 - accuracy: 0.6926 - val_loss: 0.7265 - val_accuracy: 0.6976\n",
      "Epoch 39/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6916 - accuracy: 0.6947 - val_loss: 0.7264 - val_accuracy: 0.6992\n",
      "Epoch 40/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6914 - accuracy: 0.6957 - val_loss: 0.7276 - val_accuracy: 0.6964\n",
      "Epoch 41/100\n",
      "227468/227468 [==============================] - 4s 15us/sample - loss: 0.6914 - accuracy: 0.6957 - val_loss: 0.7256 - val_accuracy: 0.7003\n",
      "Epoch 42/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6910 - accuracy: 0.6963 - val_loss: 0.7259 - val_accuracy: 0.6925\n",
      "Epoch 43/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6906 - accuracy: 0.6959 - val_loss: 0.7286 - val_accuracy: 0.7000\n",
      "Epoch 44/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6905 - accuracy: 0.6977 - val_loss: 0.7252 - val_accuracy: 0.6979\n",
      "Epoch 45/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6904 - accuracy: 0.6973 - val_loss: 0.7254 - val_accuracy: 0.6965\n",
      "Epoch 46/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6904 - accuracy: 0.6971 - val_loss: 0.7263 - val_accuracy: 0.6964\n",
      "Epoch 47/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6902 - accuracy: 0.6979 - val_loss: 0.7249 - val_accuracy: 0.6972\n",
      "Epoch 48/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6903 - accuracy: 0.6977 - val_loss: 0.7247 - val_accuracy: 0.7000\n",
      "Epoch 49/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6899 - accuracy: 0.6978 - val_loss: 0.7245 - val_accuracy: 0.7008\n",
      "Epoch 50/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6899 - accuracy: 0.6982 - val_loss: 0.7273 - val_accuracy: 0.6975\n",
      "Epoch 51/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6898 - accuracy: 0.6979 - val_loss: 0.7246 - val_accuracy: 0.6954\n",
      "Epoch 52/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6897 - accuracy: 0.6981 - val_loss: 0.7244 - val_accuracy: 0.6986\n",
      "Epoch 53/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6897 - accuracy: 0.6990 - val_loss: 0.7244 - val_accuracy: 0.6999\n",
      "Epoch 54/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6895 - accuracy: 0.6979 - val_loss: 0.7243 - val_accuracy: 0.6992\n",
      "Epoch 55/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6895 - accuracy: 0.6985 - val_loss: 0.7258 - val_accuracy: 0.6942\n",
      "Epoch 56/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6896 - accuracy: 0.6998 - val_loss: 0.7245 - val_accuracy: 0.7036\n",
      "Epoch 57/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6893 - accuracy: 0.6995 - val_loss: 0.7243 - val_accuracy: 0.6994\n",
      "Epoch 58/100\n",
      "227468/227468 [==============================] - 4s 20us/sample - loss: 0.6893 - accuracy: 0.6988 - val_loss: 0.7260 - val_accuracy: 0.6938\n",
      "Epoch 59/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6895 - accuracy: 0.6986 - val_loss: 0.7258 - val_accuracy: 0.6976\n",
      "Epoch 60/100\n",
      "227468/227468 [==============================] - 4s 18us/sample - loss: 0.6895 - accuracy: 0.6990 - val_loss: 0.7249 - val_accuracy: 0.7004\n",
      "Epoch 61/100\n",
      "227468/227468 [==============================] - 4s 20us/sample - loss: 0.6894 - accuracy: 0.6987 - val_loss: 0.7256 - val_accuracy: 0.6989\n",
      "Epoch 62/100\n",
      "227468/227468 [==============================] - 4s 20us/sample - loss: 0.6895 - accuracy: 0.6993 - val_loss: 0.7249 - val_accuracy: 0.6990\n",
      "Epoch 63/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6891 - accuracy: 0.6999 - val_loss: 0.7248 - val_accuracy: 0.7013\n",
      "Epoch 64/100\n",
      "227468/227468 [==============================] - 5s 21us/sample - loss: 0.6892 - accuracy: 0.6993 - val_loss: 0.7240 - val_accuracy: 0.7033\n",
      "Epoch 65/100\n",
      "227468/227468 [==============================] - 5s 20us/sample - loss: 0.6891 - accuracy: 0.6998 - val_loss: 0.7239 - val_accuracy: 0.7026\n",
      "Epoch 66/100\n",
      "227468/227468 [==============================] - 5s 22us/sample - loss: 0.6894 - accuracy: 0.6992 - val_loss: 0.7253 - val_accuracy: 0.6995\n",
      "Epoch 67/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6890 - accuracy: 0.6996 - val_loss: 0.7235 - val_accuracy: 0.7029\n",
      "Epoch 68/100\n",
      "227468/227468 [==============================] - 5s 23us/sample - loss: 0.6890 - accuracy: 0.6996 - val_loss: 0.7239 - val_accuracy: 0.7023\n",
      "Epoch 69/100\n",
      "227468/227468 [==============================] - 5s 22us/sample - loss: 0.6889 - accuracy: 0.6996 - val_loss: 0.7243 - val_accuracy: 0.6998\n",
      "Epoch 70/100\n",
      "227468/227468 [==============================] - 6s 28us/sample - loss: 0.6889 - accuracy: 0.6998 - val_loss: 0.7245 - val_accuracy: 0.6993\n",
      "Epoch 71/100\n",
      "227468/227468 [==============================] - 4s 18us/sample - loss: 0.6895 - accuracy: 0.6993 - val_loss: 0.7234 - val_accuracy: 0.7047\n",
      "Epoch 72/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6887 - accuracy: 0.7007 - val_loss: 0.7245 - val_accuracy: 0.7003\n",
      "Epoch 73/100\n",
      "227468/227468 [==============================] - 5s 21us/sample - loss: 0.6887 - accuracy: 0.7008 - val_loss: 0.7241 - val_accuracy: 0.7069\n",
      "Epoch 74/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6889 - accuracy: 0.7000 - val_loss: 0.7236 - val_accuracy: 0.7045\n",
      "Epoch 75/100\n",
      "227468/227468 [==============================] - 4s 15us/sample - loss: 0.6887 - accuracy: 0.7010 - val_loss: 0.7238 - val_accuracy: 0.7013\n",
      "Epoch 76/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6887 - accuracy: 0.7003 - val_loss: 0.7242 - val_accuracy: 0.6975\n",
      "Epoch 77/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6887 - accuracy: 0.7008 - val_loss: 0.7239 - val_accuracy: 0.7030\n",
      "Epoch 78/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6889 - accuracy: 0.7014 - val_loss: 0.7244 - val_accuracy: 0.6983\n",
      "Epoch 79/100\n",
      "227468/227468 [==============================] - 3s 15us/sample - loss: 0.6888 - accuracy: 0.7005 - val_loss: 0.7237 - val_accuracy: 0.7007\n",
      "Epoch 80/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6885 - accuracy: 0.7010 - val_loss: 0.7247 - val_accuracy: 0.7016\n",
      "Epoch 81/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6887 - accuracy: 0.7016 - val_loss: 0.7240 - val_accuracy: 0.7005\n",
      "Epoch 82/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6884 - accuracy: 0.7008 - val_loss: 0.7233 - val_accuracy: 0.7030\n",
      "Epoch 83/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6886 - accuracy: 0.7004 - val_loss: 0.7235 - val_accuracy: 0.7043\n",
      "Epoch 84/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6886 - accuracy: 0.7010 - val_loss: 0.7230 - val_accuracy: 0.7017\n",
      "Epoch 85/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6884 - accuracy: 0.7008 - val_loss: 0.7232 - val_accuracy: 0.7040\n",
      "Epoch 86/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6892 - accuracy: 0.7008 - val_loss: 0.7245 - val_accuracy: 0.6959\n",
      "Epoch 87/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6882 - accuracy: 0.7012 - val_loss: 0.7240 - val_accuracy: 0.7006\n",
      "Epoch 88/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6883 - accuracy: 0.7009 - val_loss: 0.7245 - val_accuracy: 0.7053\n",
      "Epoch 89/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6881 - accuracy: 0.7020 - val_loss: 0.7229 - val_accuracy: 0.7033\n",
      "Epoch 90/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6886 - accuracy: 0.7009 - val_loss: 0.7232 - val_accuracy: 0.7012\n",
      "Epoch 91/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6890 - accuracy: 0.7021 - val_loss: 0.7232 - val_accuracy: 0.7042\n",
      "Epoch 92/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6880 - accuracy: 0.7026 - val_loss: 0.7230 - val_accuracy: 0.7037\n",
      "Epoch 93/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6881 - accuracy: 0.7018 - val_loss: 0.7229 - val_accuracy: 0.7044\n",
      "Epoch 94/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6886 - accuracy: 0.7013 - val_loss: 0.7225 - val_accuracy: 0.7020\n",
      "Epoch 95/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6883 - accuracy: 0.7019 - val_loss: 0.7237 - val_accuracy: 0.7017\n",
      "Epoch 96/100\n",
      "227468/227468 [==============================] - 4s 17us/sample - loss: 0.6882 - accuracy: 0.7023 - val_loss: 0.7234 - val_accuracy: 0.7063\n",
      "Epoch 97/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6880 - accuracy: 0.7034 - val_loss: 0.7236 - val_accuracy: 0.7058\n",
      "Epoch 98/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6885 - accuracy: 0.7017 - val_loss: 0.7231 - val_accuracy: 0.7038\n",
      "Epoch 99/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6879 - accuracy: 0.7033 - val_loss: 0.7227 - val_accuracy: 0.7015\n",
      "Epoch 100/100\n",
      "227468/227468 [==============================] - 4s 16us/sample - loss: 0.6881 - accuracy: 0.7027 - val_loss: 0.7254 - val_accuracy: 0.7072\n"
     ]
    }
   ],
   "source": [
    "autoencoder.compile(metrics=['accuracy'],\n",
    "                    loss='mean_squared_error',\n",
    "                    optimizer='adam')\n",
    "\n",
    "cp = ModelCheckpoint(filepath=\"../../models/autoencoder_sensor_anomaly_detection_fully_trained_100_epochs.h5\",\n",
    "                               save_best_only=True,\n",
    "                               verbose=0)\n",
    "\n",
    "tb = TensorBoard(log_dir='./logs',\n",
    "                histogram_freq=0,\n",
    "                write_graph=True,\n",
    "                write_images=True)\n",
    "\n",
    "history = autoencoder.fit(train_x, train_x, # Autoencoder => Input == Output dimensions!\n",
    "                    epochs=nb_epoch,\n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    validation_data=(test_x, test_x), # Autoencoder => Input == Output dimensions!\n",
    "                    verbose=1,\n",
    "                    callbacks=[cp, tb]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "autoencoder = load_model('../../models/autoencoder_sensor_anomaly_detection.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'], linewidth=2, label='Train')\n",
    "plt.plot(history['val_loss'], linewidth=2, label='Test')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "#plt.ylim(ymin=0.70,ymax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reconstruction_error</th>\n",
       "      <th>True_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56962.000000</td>\n",
       "      <td>56962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.748841</td>\n",
       "      <td>0.002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.277344</td>\n",
       "      <td>0.044887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.041970</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.258069</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.398641</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.639001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>194.557384</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Reconstruction_error    True_class\n",
       "count          56962.000000  56962.000000\n",
       "mean               0.748841      0.002019\n",
       "std                3.277344      0.044887\n",
       "min                0.041970      0.000000\n",
       "25%                0.258069      0.000000\n",
       "50%                0.398641      0.000000\n",
       "75%                0.639001      0.000000\n",
       "max              194.557384      1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x_predictions = autoencoder.predict(test_x)\n",
    "mse = np.mean(np.power(test_x - test_x_predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'Reconstruction_error': mse,\n",
    "                        'True_class': test_y})\n",
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_pos_rate, true_pos_rate, thresholds = roc_curve(error_df.True_class, error_df.Reconstruction_error)\n",
    "roc_auc = auc(false_pos_rate, true_pos_rate,)\n",
    "\n",
    "plt.plot(false_pos_rate, true_pos_rate, linewidth=5, label='AUC = %0.3f'% roc_auc)\n",
    "plt.plot([0,1],[0,1], linewidth=5)\n",
    "\n",
    "plt.xlim([-0.01, 1])\n",
    "plt.ylim([0, 1.01])\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Receiver operating characteristic curve (ROC)')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision_rt, recall_rt, threshold_rt = precision_recall_curve(error_df.True_class, error_df.Reconstruction_error)\n",
    "plt.plot(recall_rt, precision_rt, linewidth=5, label='Precision-Recall curve')\n",
    "plt.title('Recall vs Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(threshold_rt, precision_rt[1:], label=\"Precision\",linewidth=5)\n",
    "plt.plot(threshold_rt, recall_rt[1:], label=\"Recall\",linewidth=5)\n",
    "plt.title('Precision and recall for different threshold values')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision/Recall')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x626.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_fixed = 5\n",
    "groups = error_df.groupby('True_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.Reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Fraud\" if name == 1 else \"Normal\")\n",
    "ax.hlines(threshold_fixed, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = [1 if e > threshold_fixed else 0 for e in error_df.Reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.True_class, pred_y)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
